Ammonia volatilization and nitrogen runoff losses from moso bamboo forests after different fertilization practices
Bibliographic record
Abstract
Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) is a major giant bamboo species. Unreasonable fertilization and nitrogen (N) loss is a serious problem. A field experiment was conducted to determine the effects of application methods (furrow and hole) and depths (0–20 and 20–40 cm) on ammonia (NH3) volatilization and N runoff losses from June to December, 2015. Ammonia volatilization was detected as a single peak curve after fertilization. Three weeks later, fluxes of all fertilization treatments were similar to those of the control and remained stable. After the experiment, the cumulative NH3 volatilization was 17.2–21.4 kg·ha−1. Approximately 90% of NH3 volatilization occurred within the first two weeks. The NH4+-N loss was higher than the NO3−-N loss, and the total runoff losses were relatively low. For the same application method, with the increase of application depth, the NH3 volatilization and N runoff losses reduced; for the same application depth, the NH3 volatilization and N runoff losses after furrow application were lower than those after hole application. Therefore, the deep and furrow application methods were effective in reducing N loss through NH3 volatilization and runoff. In the present study, furrow application at a 20–40 cm depth has been appropriate for field practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".